Pandas Profiling Report

Dataset statistics

Number of variables	21
Number of observations	36574
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	58
Duplicate rows (%)	0.2%
Total size in memory	5.9 MiB
Average record size in memory	168.0 B

Variable types

Categorical	8
Numeric	11
DateTime	2

Warnings

Dataset has 58 (0.2%) duplicate rows	Duplicates
`land_area` is highly correlated with `bathroom`	High correlation
`living_room` is highly correlated with `bedroom`	High correlation
`bedroom` is highly correlated with `living_room` and 1 other fields	High correlation
`bathroom` is highly correlated with `land_area` and 1 other fields	High correlation
`land_area` is highly correlated with `bedroom` and 2 other fields	High correlation
`living_room` is highly correlated with `bedroom` and 1 other fields	High correlation
`bedroom` is highly correlated with `land_area` and 3 other fields	High correlation
`bathroom` is highly correlated with `land_area` and 3 other fields	High correlation
`property_area` is highly correlated with `land_area` and 2 other fields	High correlation
`built_year` is highly correlated with `price_per_sqm`	High correlation
`price_per_sqm` is highly correlated with `built_year`	High correlation
`living_room` is highly correlated with `bedroom` and 1 other fields	High correlation
`bedroom` is highly correlated with `living_room` and 1 other fields	High correlation
`bathroom` is highly correlated with `living_room` and 1 other fields	High correlation
`property_area` is highly correlated with `bedroom` and 2 other fields	High correlation
`parking_type` is highly correlated with `is_managed` and 1 other fields	High correlation
`price_per_sqm` is highly correlated with `land_area`	High correlation
`property_type` is highly correlated with `built_year` and 3 other fields	High correlation
`built_year` is highly correlated with `property_type` and 3 other fields	High correlation
`bedroom` is highly correlated with `property_area` and 1 other fields	High correlation
`is_managed` is highly correlated with `parking_type` and 4 other fields	High correlation
`total_floor` is highly correlated with `property_type` and 3 other fields	High correlation
`trading_target` is highly correlated with `parking_type` and 3 other fields	High correlation
`land_use` is highly correlated with `purpose`	High correlation
`land_area` is highly correlated with `property_area` and 1 other fields	High correlation
`purpose` is highly correlated with `property_type` and 1 other fields	High correlation
`bathroom` is highly correlated with `property_area` and 1 other fields	High correlation
`parking_type` is highly correlated with `trading_target`	High correlation
`is_managed` is highly correlated with `property_type`	High correlation
`trading_target` is highly correlated with `parking_type`	High correlation
`property_type` is highly correlated with `is_managed`	High correlation
`parking_area` is highly skewed (γ1 = 185.1225046)	Skewed
`living_room` has 1549 (4.2%) zeros	Zeros
`bedroom` has 1236 (3.4%) zeros	Zeros
`bathroom` has 1108 (3.0%) zeros	Zeros
`parking_area` has 27514 (75.2%) zeros	Zeros
`parking_price` has 34744 (95.0%) zeros	Zeros

Reproduction

Analysis started	2021-07-18 05:23:49.509466
Analysis finished	2021-07-18 05:24:19.920450
Duration	30.41 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

purpose
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

住家用	33643
住商用	2244
商業用	604
國民住宅	73
工業用	10

Length

Max length	4
Median length	3
Mean length	3.001995953
Min length	3

Characters and Unicode

Total characters	109795
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	住家用
2nd row	住家用
3rd row	住家用
4th row	住家用
5th row	住家用

Common Values

Value	Count	Frequency (%)
住家用	33643	92.0%
住商用	2244	6.1%
商業用	604	1.7%
國民住宅	73	0.2%
工業用	10	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
住家用	33643	92.0%
住商用	2244	6.1%
商業用	604	1.7%
國民住宅	73	0.2%
工業用	10	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
用	36501	33.2%
住	35960	32.8%
家	33643	30.6%
商	2848	2.6%
業	614	0.6%
國	73	0.1%
民	73	0.1%
宅	73	0.1%
工	10	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	109795	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
用	36501	33.2%
住	35960	32.8%
家	33643	30.6%
商	2848	2.6%
業	614	0.6%
國	73	0.1%
民	73	0.1%
宅	73	0.1%
工	10	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Han	109795	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
用	36501	33.2%
住	35960	32.8%
家	33643	30.6%
商	2848	2.6%
業	614	0.6%
國	73	0.1%
民	73	0.1%
宅	73	0.1%
工	10	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
CJK	109795	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
用	36501	33.2%
住	35960	32.8%
家	33643	30.6%
商	2848	2.6%
業	614	0.6%
國	73	0.1%
民	73	0.1%
宅	73	0.1%
工	10	< 0.1%

trading_target
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

房地(土地+建物)	19687
房地(土地+建物)+車位	16887

Length

Max length	12
Median length	9
Mean length	10.38516432
Min length	9

Characters and Unicode

Total characters	379827
Distinct characters	10
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	房地(土地+建物)+車位
2nd row	房地(土地+建物)+車位
3rd row	房地(土地+建物)+車位
4th row	房地(土地+建物)+車位
5th row	房地(土地+建物)

Common Values

Value	Count	Frequency (%)
房地(土地+建物)	19687	53.8%
房地(土地+建物)+車位	16887	46.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
房地(土地+建物	19687	53.8%
房地(土地+建物)+車位	16887	46.2%

Most occurring characters

Value	Count	Frequency (%)
地	73148	19.3%
+	53461	14.1%
房	36574	9.6%
(	36574	9.6%
土	36574	9.6%
建	36574	9.6%
物	36574	9.6%
)	36574	9.6%
車	16887	4.4%
位	16887	4.4%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	253218	66.7%
Math Symbol	53461	14.1%
Open Punctuation	36574	9.6%
Close Punctuation	36574	9.6%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
地	73148	28.9%
房	36574	14.4%
土	36574	14.4%
建	36574	14.4%
物	36574	14.4%
車	16887	6.7%
位	16887	6.7%

Open Punctuation

Value	Count	Frequency (%)
(	36574	100.0%

Math Symbol

Value	Count	Frequency (%)
+	53461	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	36574	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Han	253218	66.7%
Common	126609	33.3%

Most frequent character per script

Han

Value	Count	Frequency (%)
地	73148	28.9%
房	36574	14.4%
土	36574	14.4%
建	36574	14.4%
物	36574	14.4%
車	16887	6.7%
位	16887	6.7%

Common

Value	Count	Frequency (%)
+	53461	42.2%
(	36574	28.9%
)	36574	28.9%

Most occurring blocks

Value	Count	Frequency (%)
CJK	253218	66.7%
ASCII	126609	33.3%

Most frequent character per block

CJK

Value	Count	Frequency (%)
地	73148	28.9%
房	36574	14.4%
土	36574	14.4%
建	36574	14.4%
物	36574	14.4%
車	16887	6.7%
位	16887	6.7%

ASCII

Value	Count	Frequency (%)
+	53461	42.2%
(	36574	28.9%
)	36574	28.9%

land_area
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5232
Distinct (%)	14.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	24.97321841

Minimum	0.01
Maximum	2140.1
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0.01
5-th percentile	4.56
Q1	10.49
median	16.67
Q3	26.21
95-th percentile	81
Maximum	2140.1
Range	2140.09
Interquartile range (IQR)	15.72

Descriptive statistics

Standard deviation	32.36453646
Coefficient of variation (CV)	1.295969784
Kurtosis	1028.379018
Mean	24.97321841
Median Absolute Deviation (MAD)	7.21
Skewness	18.6802785
Sum	913370.49
Variance	1047.46322
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
64	77	0.2%
26.9	76	0.2%
12.03	71	0.2%
68	69	0.2%
19.03	69	0.2%
9.22	62	0.2%
10.82	62	0.2%
62	62	0.2%
6.09	61	0.2%
65	60	0.2%
Other values (5222)	35905	98.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.01	1	< 0.1%
0.09	1	< 0.1%
0.35	2	< 0.1%
0.6	5	< 0.1%
0.73	9	< 0.1%
0.85	16	< 0.1%
0.98	1	< 0.1%
1.08	1	< 0.1%
1.14	3	< 0.1%
1.18	1	< 0.1%

Value	Count	Frequency (%)
2140.1	1	< 0.1%
2136	1	< 0.1%
711	1	< 0.1%
704.5	2	< 0.1%
578	1	< 0.1%
556.53	1	< 0.1%
536.68	1	< 0.1%
495.08	1	< 0.1%
466	1	< 0.1%
454.2	1	< 0.1%

property_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

住宅大樓(11層含以上有電梯)	24979
透天厝	4309
華廈(10層含以下有電梯)	3383
公寓(5樓含以下無電梯)	2277
套房(1房1廳1衛)	1626

Length

Max length	15
Median length	15
Mean length	12.9921529
Min length	3

Characters and Unicode

Total characters	475175
Distinct characters	29
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	住宅大樓(11層含以上有電梯)
2nd row	華廈(10層含以下有電梯)
3rd row	住宅大樓(11層含以上有電梯)
4th row	住宅大樓(11層含以上有電梯)
5th row	套房(1房1廳1衛)

Common Values

Value	Count	Frequency (%)
住宅大樓(11層含以上有電梯)	24979	68.3%
透天厝	4309	11.8%
華廈(10層含以下有電梯)	3383	9.2%
公寓(5樓含以下無電梯)	2277	6.2%
套房(1房1廳1衛)	1626	4.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
住宅大樓(11層含以上有電梯	24979	68.3%
透天厝	4309	11.8%
華廈(10層含以下有電梯	3383	9.2%
公寓(5樓含以下無電梯	2277	6.2%
套房(1房1廳1衛	1626	4.4%

Most occurring characters

Value	Count	Frequency (%)
1	58219	12.3%
(	32265	6.8%
)	32265	6.8%
含	30639	6.4%
以	30639	6.4%
電	30639	6.4%
梯	30639	6.4%
層	28362	6.0%
有	28362	6.0%
樓	27256	5.7%
Other values (19)	145890	30.7%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	346766	73.0%
Decimal Number	63879	13.4%
Open Punctuation	32265	6.8%
Close Punctuation	32265	6.8%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
含	30639	8.8%
以	30639	8.8%
電	30639	8.8%
梯	30639	8.8%
層	28362	8.2%
有	28362	8.2%
樓	27256	7.9%
住	24979	7.2%
宅	24979	7.2%
大	24979	7.2%
Other values (14)	65293	18.8%

Decimal Number

Value	Count	Frequency (%)
1	58219	91.1%
0	3383	5.3%
5	2277	3.6%

Open Punctuation

Value	Count	Frequency (%)
(	32265	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	32265	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Han	346766	73.0%
Common	128409	27.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
含	30639	8.8%
以	30639	8.8%
電	30639	8.8%
梯	30639	8.8%
層	28362	8.2%
有	28362	8.2%
樓	27256	7.9%
住	24979	7.2%
宅	24979	7.2%
大	24979	7.2%
Other values (14)	65293	18.8%

Common

Value	Count	Frequency (%)
1	58219	45.3%
(	32265	25.1%
)	32265	25.1%
0	3383	2.6%
5	2277	1.8%

Most occurring blocks

Value	Count	Frequency (%)
CJK	346766	73.0%
ASCII	128409	27.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
含	30639	8.8%
以	30639	8.8%
電	30639	8.8%
梯	30639	8.8%
層	28362	8.2%
有	28362	8.2%
樓	27256	7.9%
住	24979	7.2%
宅	24979	7.2%
大	24979	7.2%
Other values (14)	65293	18.8%

ASCII

Value	Count	Frequency (%)
1	58219	45.3%
(	32265	25.1%
)	32265	25.1%
0	3383	2.6%
5	2277	1.8%

living_room
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.740033904

Minimum	0
Maximum	22
Zeros	1549
Zeros (%)	4.2%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	2
median	2
Q3	2
95-th percentile	2
Maximum	22
Range	22
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5883102435
Coefficient of variation (CV)	0.3381027474
Kurtosis	44.99257646
Mean	1.740033904
Median Absolute Deviation (MAD)	0
Skewness	0.4228411536
Sum	63640
Variance	0.3461089426
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
2	27314	74.7%
1	7165	19.6%
0	1549	4.2%
3	414	1.1%
4	105	0.3%
5	11	< 0.1%
6	6	< 0.1%
7	5	< 0.1%
9	2	< 0.1%
22	1	< 0.1%
Other values (2)	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1549	4.2%
1	7165	19.6%
2	27314	74.7%
3	414	1.1%
4	105	0.3%
5	11	< 0.1%
6	6	< 0.1%
7	5	< 0.1%
8	1	< 0.1%
9	2	< 0.1%

Value	Count	Frequency (%)
22	1	< 0.1%
11	1	< 0.1%
9	2	< 0.1%
8	1	< 0.1%
7	5	< 0.1%
6	6	< 0.1%
5	11	< 0.1%
4	105	0.3%
3	414	1.1%
2	27314	74.7%

bedroom
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	25
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.923333516

Minimum	0
Maximum	52
Zeros	1236
Zeros (%)	3.4%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	2
median	3
Q3	3
95-th percentile	5
Maximum	52
Range	52
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.311398753
Coefficient of variation (CV)	0.4485970371
Kurtosis	100.6856806
Mean	2.923333516
Median Absolute Deviation (MAD)	1
Skewness	3.848160234
Sum	106918
Variance	1.719766691
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=25)

Value	Count	Frequency (%)
3	16638	45.5%
2	6940	19.0%
4	6630	18.1%
1	2673	7.3%
5	1626	4.4%
0	1236	3.4%
6	557	1.5%
7	142	0.4%
8	68	0.2%
9	18	< 0.1%
Other values (15)	46	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1236	3.4%
1	2673	7.3%
2	6940	19.0%
3	16638	45.5%
4	6630	18.1%
5	1626	4.4%
6	557	1.5%
7	142	0.4%
8	68	0.2%
9	18	< 0.1%

Value	Count	Frequency (%)
52	1	< 0.1%
44	1	< 0.1%
29	1	< 0.1%
24	1	< 0.1%
23	3	< 0.1%
22	4	< 0.1%
21	1	< 0.1%
18	2	< 0.1%
17	1	< 0.1%
16	3	< 0.1%

bathroom
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	24
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.908897031

Minimum	0
Maximum	50
Zeros	1108
Zeros (%)	3.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	2
Q3	2
95-th percentile	4
Maximum	50
Range	50
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.096129372
Coefficient of variation (CV)	0.5742213198
Kurtosis	164.2253516
Mean	1.908897031
Median Absolute Deviation (MAD)	0
Skewness	6.686025097
Sum	69816
Variance	1.201499601
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
2	20878	57.1%
1	9825	26.9%
3	2604	7.1%
4	1345	3.7%
0	1108	3.0%
5	447	1.2%
6	195	0.5%
7	87	0.2%
8	44	0.1%
9	8	< 0.1%
Other values (14)	33	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1108	3.0%
1	9825	26.9%
2	20878	57.1%
3	2604	7.1%
4	1345	3.7%
5	447	1.2%
6	195	0.5%
7	87	0.2%
8	44	0.1%
9	8	< 0.1%

Value	Count	Frequency (%)
50	1	< 0.1%
30	1	< 0.1%
24	1	< 0.1%
23	6	< 0.1%
22	3	< 0.1%
20	1	< 0.1%
18	4	< 0.1%
16	3	< 0.1%
15	1	< 0.1%
14	3	< 0.1%

partition
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

有	35450
無	1124

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	36574
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	有
2nd row	有
3rd row	有
4th row	有
5th row	有

Common Values

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

Most occurring characters

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	36574	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

Most occurring scripts

Value	Count	Frequency (%)
Han	36574	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

Most occurring blocks

Value	Count	Frequency (%)
CJK	36574	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
有	35450	96.9%
無	1124	3.1%

property_area
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	15855
Distinct (%)	43.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	145.6978211

Minimum	0.02
Maximum	4119.9
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0.02
5-th percentile	44.87
Q1	89.56
median	128.815
Q3	171.81
95-th percentile	317.67
Maximum	4119.9
Range	4119.88
Interquartile range (IQR)	82.25

Descriptive statistics

Standard deviation	89.26891038
Coefficient of variation (CV)	0.6126990074
Kurtosis	134.5069567
Mean	145.6978211
Median Absolute Deviation (MAD)	40.575
Skewness	5.040330808
Sum	5328752.11
Variance	7968.938361
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
73.65	67	0.2%
236.4	37	0.1%
73.01	36	0.1%
180.57	36	0.1%
226.37	34	0.1%
73.53	33	0.1%
74.44	33	0.1%
379.17	31	0.1%
80.05	31	0.1%
49.42	28	0.1%
Other values (15845)	36208	99.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.02	1	< 0.1%
5.26	1	< 0.1%
6.27	1	< 0.1%
9.18	1	< 0.1%
9.26	1	< 0.1%
9.72	1	< 0.1%
11.82	1	< 0.1%
12.36	1	< 0.1%
12.75	1	< 0.1%
13.32	1	< 0.1%

Value	Count	Frequency (%)
4119.9	1	< 0.1%
2687.98	1	< 0.1%
1542.21	1	< 0.1%
1367.53	1	< 0.1%
1251.49	1	< 0.1%
1165.64	1	< 0.1%
1159.47	1	< 0.1%
1080.77	1	< 0.1%
903.83	1	< 0.1%
873.25	1	< 0.1%

is_managed
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

有	27716
無	8858

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	36574
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	有
2nd row	有
3rd row	有
4th row	有
5th row	有

Common Values

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

Most occurring characters

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	36574	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

Most occurring scripts

Value	Count	Frequency (%)
Han	36574	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

Most occurring blocks

Value	Count	Frequency (%)
CJK	36574	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
有	27716	75.8%
無	8858	24.2%

total_floor
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	39
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.74487341

Minimum	1
Maximum	85
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	8
median	14
Q3	15
95-th percentile	27
Maximum	85
Range	84
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	7.807881711
Coefficient of variation (CV)	0.568057739
Kurtosis	15.84676228
Mean	13.74487341
Median Absolute Deviation (MAD)	3
Skewness	2.196963602
Sum	502705
Variance	60.96301681
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=39)

Value	Count	Frequency (%)
15	8100	22.1%
14	4833	13.2%
5	2864	7.8%
12	2290	6.3%
13	1883	5.1%
4	1717	4.7%
7	1599	4.4%
2	1422	3.9%
24	1258	3.4%
16	1042	2.8%
Other values (29)	9566	26.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	40	0.1%
2	1422	3.9%
3	644	1.8%
4	1717	4.7%
5	2864	7.8%
6	358	1.0%
7	1599	4.4%
8	700	1.9%
9	487	1.3%
10	630	1.7%

Value	Count	Frequency (%)
85	87	0.2%
42	1	< 0.1%
41	32	0.1%
39	78	0.2%
37	3	< 0.1%
36	48	0.1%
35	301	0.8%
34	26	0.1%
33	155	0.4%
30	55	0.2%

parking_area
Real number (ℝ_≥0)

SKEWED
ZEROS

Distinct	1904
Distinct (%)	5.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.722022475

Minimum	0
Maximum	17098
Zeros	27514
Zeros (%)	75.2%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	32.85
Maximum	17098
Range	17098
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	90.34744507
Coefficient of variation (CV)	13.44051517
Kurtosis	35019.35907
Mean	6.722022475
Median Absolute Deviation (MAD)	0
Skewness	185.1225046
Sum	245851.25
Variance	8162.660831
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	27514	75.2%
25.92	134	0.4%
23.17	104	0.3%
30.5	100	0.3%
17.98	95	0.3%
19.14	92	0.3%
19.9	90	0.2%
19.72	82	0.2%
17.89	65	0.2%
55.7	60	0.2%
Other values (1894)	8238	22.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	27514	75.2%
0.03	1	< 0.1%
0.15	2	< 0.1%
1	1	< 0.1%
1.28	1	< 0.1%
1.29	3	< 0.1%
2.24	1	< 0.1%
2.27	1	< 0.1%
2.6	1	< 0.1%
3	1	< 0.1%

Value	Count	Frequency (%)
17098	1	< 0.1%
274.12	1	< 0.1%
150.3	1	< 0.1%
141.12	1	< 0.1%
125.64	1	< 0.1%
122.47	1	< 0.1%
111.4	1	< 0.1%
108.93	1	< 0.1%
108.26	1	< 0.1%
108.15	1	< 0.1%

parking_price
Real number (ℝ_≥0)

ZEROS

Distinct	166
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	100366.6575

Minimum	0
Maximum	10000000
Zeros	34744
Zeros (%)	95.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	46.15
Maximum	10000000
Range	10000000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	532270.8544
Coefficient of variation (CV)	5.30326373
Kurtosis	61.15362116
Mean	100366.6575
Median Absolute Deviation (MAD)	0
Skewness	7.102265919
Sum	3670810131
Variance	2.833122624 × 10¹¹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	34744	95.0%
1000000	116	0.3%
1200000	109	0.3%
1500000	108	0.3%
1150000	70	0.2%
800000	68	0.2%
1300000	64	0.2%
1600000	60	0.2%
1800000	47	0.1%
1050000	42	0.1%
Other values (156)	1146	3.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	34744	95.0%
1	1	< 0.1%
130	1	< 0.1%
150000	1	< 0.1%
250000	1	< 0.1%
270000	1	< 0.1%
280000	1	< 0.1%
300000	1	< 0.1%
400000	2	< 0.1%
450000	2	< 0.1%

Value	Count	Frequency (%)
10000000	1	< 0.1%
8800000	1	< 0.1%
8200000	1	< 0.1%
7600000	1	< 0.1%
7500000	2	< 0.1%
7400000	1	< 0.1%
7350000	1	< 0.1%
7200000	3	< 0.1%
7050000	4	< 0.1%
7000000	3	< 0.1%

parking_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	8
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

無	19683
坡道平面	12153
坡道機械	3716
升降機械	676
升降平面	109
Other values (3)	237

Length

Max length	4
Median length	1
Mean length	2.379914693
Min length	1

Characters and Unicode

Total characters	87043
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	坡道平面
2nd row	坡道平面
3rd row	坡道平面
4th row	坡道平面
5th row	無

Common Values

Value	Count	Frequency (%)
無	19683	53.8%
坡道平面	12153	33.2%
坡道機械	3716	10.2%
升降機械	676	1.8%
升降平面	109	0.3%
其他	102	0.3%
一樓平面	100	0.3%
塔式車位	35	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
無	19683	53.8%
坡道平面	12153	33.2%
坡道機械	3716	10.2%
升降機械	676	1.8%
升降平面	109	0.3%
其他	102	0.3%
一樓平面	100	0.3%
塔式車位	35	0.1%

Most occurring characters

Value	Count	Frequency (%)
無	19683	22.6%
坡	15869	18.2%
道	15869	18.2%
平	12362	14.2%
面	12362	14.2%
機	4392	5.0%
械	4392	5.0%
升	785	0.9%
降	785	0.9%
其	102	0.1%
Other values (7)	442	0.5%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	87043	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
無	19683	22.6%
坡	15869	18.2%
道	15869	18.2%
平	12362	14.2%
面	12362	14.2%
機	4392	5.0%
械	4392	5.0%
升	785	0.9%
降	785	0.9%
其	102	0.1%
Other values (7)	442	0.5%

Most occurring scripts

Value	Count	Frequency (%)
Han	87043	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
無	19683	22.6%
坡	15869	18.2%
道	15869	18.2%
平	12362	14.2%
面	12362	14.2%
機	4392	5.0%
械	4392	5.0%
升	785	0.9%
降	785	0.9%
其	102	0.1%
Other values (7)	442	0.5%

Most occurring blocks

Value	Count	Frequency (%)
CJK	87043	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
無	19683	22.6%
坡	15869	18.2%
道	15869	18.2%
平	12362	14.2%
面	12362	14.2%
機	4392	5.0%
械	4392	5.0%
升	785	0.9%
降	785	0.9%
其	102	0.1%
Other values (7)	442	0.5%

land_use
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

住	24265
商	11951
其他	335
工	19
農	4

Length

Max length	2
Median length	1
Mean length	1.009159512
Min length	1

Characters and Unicode

Total characters	36909
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	商
2nd row	住
3rd row	住
4th row	商
5th row	商

Common Values

Value	Count	Frequency (%)
住	24265	66.3%
商	11951	32.7%
其他	335	0.9%
工	19	0.1%
農	4	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
住	24265	66.3%
商	11951	32.7%
其他	335	0.9%
工	19	0.1%
農	4	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
住	24265	65.7%
商	11951	32.4%
其	335	0.9%
他	335	0.9%
工	19	0.1%
農	4	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	36909	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
住	24265	65.7%
商	11951	32.4%
其	335	0.9%
他	335	0.9%
工	19	0.1%
農	4	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Han	36909	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
住	24265	65.7%
商	11951	32.4%
其	335	0.9%
他	335	0.9%
工	19	0.1%
農	4	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
CJK	36909	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
住	24265	65.7%
商	11951	32.4%
其	335	0.9%
他	335	0.9%
工	19	0.1%
農	4	< 0.1%

district
Categorical

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

三民區	6757
左營區	6218
鳳山區	6088
鼓山區	5883
楠梓區	4666
Other values (4)	6962

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	109722
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	新興區
2nd row	鳳山區
3rd row	鼓山區
4th row	三民區
5th row	三民區

Common Values

Value	Count	Frequency (%)
三民區	6757	18.5%
左營區	6218	17.0%
鳳山區	6088	16.6%
鼓山區	5883	16.1%
楠梓區	4666	12.8%
前鎮區	2937	8.0%
苓雅區	2264	6.2%
新興區	916	2.5%
前金區	845	2.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
三民區	6757	18.5%
左營區	6218	17.0%
鳳山區	6088	16.6%
鼓山區	5883	16.1%
楠梓區	4666	12.8%
前鎮區	2937	8.0%
苓雅區	2264	6.2%
新興區	916	2.5%
前金區	845	2.3%

Most occurring characters

Value	Count	Frequency (%)
區	36574	33.3%
山	11971	10.9%
三	6757	6.2%
民	6757	6.2%
左	6218	5.7%
營	6218	5.7%
鳳	6088	5.5%
鼓	5883	5.4%
楠	4666	4.3%
梓	4666	4.3%
Other values (7)	13924	12.7%

Most occurring categories

Value	Count	Frequency (%)
Other Letter	109722	100.0%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
區	36574	33.3%
山	11971	10.9%
三	6757	6.2%
民	6757	6.2%
左	6218	5.7%
營	6218	5.7%
鳳	6088	5.5%
鼓	5883	5.4%
楠	4666	4.3%
梓	4666	4.3%
Other values (7)	13924	12.7%

Most occurring scripts

Value	Count	Frequency (%)
Han	109722	100.0%

Most frequent character per script

Han

Value	Count	Frequency (%)
區	36574	33.3%
山	11971	10.9%
三	6757	6.2%
民	6757	6.2%
左	6218	5.7%
營	6218	5.7%
鳳	6088	5.5%
鼓	5883	5.4%
楠	4666	4.3%
梓	4666	4.3%
Other values (7)	13924	12.7%

Most occurring blocks

Value	Count	Frequency (%)
CJK	109722	100.0%

Most frequent character per block

CJK

Value	Count	Frequency (%)
區	36574	33.3%
山	11971	10.9%
三	6757	6.2%
民	6757	6.2%
左	6218	5.7%
營	6218	5.7%
鳳	6088	5.5%
鼓	5883	5.4%
楠	4666	4.3%
梓	4666	4.3%
Other values (7)	13924	12.7%

trading_date
Date

Distinct	2204
Distinct (%)	6.0%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

Minimum	2012-01-04 00:00:00
Maximum	2020-04-28 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

trading_year
Real number (ℝ_≥0)

Distinct	9
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2016.756248

Minimum	2012
Maximum	2020
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	2012
5-th percentile	2014
Q1	2015
median	2017
Q3	2018
95-th percentile	2019
Maximum	2020
Range	8
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.700337546
Coefficient of variation (CV)	0.0008431051339
Kurtosis	-1.117668552
Mean	2016.756248
Median Absolute Deviation (MAD)	2
Skewness	0.08912163036
Sum	73760843
Variance	2.891147771
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
2015	8143	22.3%
2019	6326	17.3%
2016	6246	17.1%
2017	5869	16.0%
2018	5851	16.0%
2014	2784	7.6%
2020	1271	3.5%
2013	73	0.2%
2012	11	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2012	11	< 0.1%
2013	73	0.2%
2014	2784	7.6%
2015	8143	22.3%
2016	6246	17.1%
2017	5869	16.0%
2018	5851	16.0%
2019	6326	17.3%
2020	1271	3.5%

Value	Count	Frequency (%)
2020	1271	3.5%
2019	6326	17.3%
2018	5851	16.0%
2017	5869	16.0%
2016	6246	17.1%
2015	8143	22.3%
2014	2784	7.6%
2013	73	0.2%
2012	11	< 0.1%

built_date
Date

Distinct	4312
Distinct (%)	11.8%
Missing	0
Missing (%)	0.0%
Memory size	285.9 KiB

Minimum	1930-07-01 00:00:00
Maximum	2020-03-17 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

built_year
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	74
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1999.858342

Minimum	1930
Maximum	2020
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	1930
5-th percentile	1978
Q1	1994
median	1999
Q3	2009
95-th percentile	2015
Maximum	2020
Range	90
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	11.45924896
Coefficient of variation (CV)	0.005730030335
Kurtosis	0.3938037068
Mean	1999.858342
Median Absolute Deviation (MAD)	7
Skewness	-0.6853610139
Sum	73142819
Variance	131.3143868
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2014	4118	11.3%
1994	2859	7.8%
1995	2306	6.3%
1996	1909	5.2%
2006	1899	5.2%
2005	1630	4.5%
1993	1403	3.8%
1997	1389	3.8%
2015	1237	3.4%
1998	1218	3.3%
Other values (64)	16606	45.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1930	1	< 0.1%
1931	2	< 0.1%
1934	2	< 0.1%
1941	2	< 0.1%
1943	1	< 0.1%
1946	2	< 0.1%
1947	1	< 0.1%
1950	1	< 0.1%
1953	3	< 0.1%
1954	4	< 0.1%

Value	Count	Frequency (%)
2020	3	< 0.1%
2019	69	0.2%
2018	84	0.2%
2017	133	0.4%
2016	371	1.0%
2015	1237	3.4%
2014	4118	11.3%
2013	1098	3.0%
2012	956	2.6%
2011	620	1.7%

price_per_sqm
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	26688
Distinct (%)	73.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	52348.82673

Minimum	0
Maximum	1048343
Zeros	4
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	285.9 KiB

Quantile statistics

Minimum	0
5-th percentile	27548
Q1	38548.75
median	48344.5
Q3	62482
95-th percentile	85949.4
Maximum	1048343
Range	1048343
Interquartile range (IQR)	23933.25

Descriptive statistics

Standard deviation	22885.36321
Coefficient of variation (CV)	0.4371705086
Kurtosis	308.7067158
Mean	52348.82673
Median Absolute Deviation (MAD)	11476.5
Skewness	9.440632576
Sum	1914605989
Variance	523739849.3
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
20417	13	< 0.1%
33944	9	< 0.1%
35714	8	< 0.1%
43478	8	< 0.1%
36660	7	< 0.1%
42373	7	< 0.1%
43830	7	< 0.1%
47619	7	< 0.1%
45201	7	< 0.1%
36320	7	< 0.1%
Other values (26678)	36494	99.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4	< 0.1%
2677	1	< 0.1%
4352	1	< 0.1%
4515	1	< 0.1%
4804	1	< 0.1%
7639	1	< 0.1%
9016	1	< 0.1%
9400	1	< 0.1%
9461	1	< 0.1%
10583	1	< 0.1%

Value	Count	Frequency (%)
1048343	1	< 0.1%
1012085	1	< 0.1%
874381	1	< 0.1%
851927	1	< 0.1%
730544	1	< 0.1%
542274	1	< 0.1%
493827	1	< 0.1%
444705	1	< 0.1%
351617	1	< 0.1%
344848	1	< 0.1%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	purpose	trading_target	land_area	property_type	living_room	bedroom	bathroom	partition	property_area	is_managed	total_floor	parking_area	parking_type	land_use	district	trading_date	trading_year	built_date	built_year	price_per_sqm
0	住家用	房地(土地+建物)+車位	12.94	住宅大樓(11層含以上有電梯)	2	3	2	有	205.37	有	15	59.37	坡道平面	商	新興區	2016-10-27	2016	2016-06-17	2016	76204.0
1	住家用	房地(土地+建物)+車位	16.76	華廈(10層含以下有電梯)	2	3	2	有	110.53	有	8	0.00	坡道平面	住	鳳山區	2019-10-18	2019	1994-03-04	1994	44603.0
2	住家用	房地(土地+建物)+車位	24.86	住宅大樓(11層含以上有電梯)	2	3	2	有	188.07	有	13	0.00	坡道平面	住	鼓山區	2016-04-19	2016	1996-11-20	1996	56681.0
3	住家用	房地(土地+建物)+車位	15.26	住宅大樓(11層含以上有電梯)	2	4	2	有	166.03	有	12	23.08	坡道平面	商	三民區	2014-10-21	2014	1998-09-02	1998	37644.0
4	住家用	房地(土地+建物)	2.50	套房(1房1廳1衛)	1	1	1	有	28.00	有	14	0.00	無	商	三民區	2015-07-30	2015	1999-10-14	1999	33929.0
5	住家用	房地(土地+建物)	98.00	透天厝	2	4	5	有	103.29	無	2	0.00	無	商	前鎮區	2019-09-16	2019	1966-08-20	1966	58089.0
6	住家用	房地(土地+建物)+車位	17.15	住宅大樓(11層含以上有電梯)	2	4	2	有	163.85	有	15	0.00	坡道機械	住	鼓山區	2017-07-06	2017	2015-03-11	2015	73238.0
7	住家用	房地(土地+建物)	19.34	住宅大樓(11層含以上有電梯)	2	3	1	有	106.44	有	18	0.00	無	住	左營區	2015-03-24	2015	1985-11-29	1985	31755.0
8	住家用	房地(土地+建物)	7.71	住宅大樓(11層含以上有電梯)	2	2	1	有	84.22	有	14	0.00	無	商	鼓山區	2018-12-16	2018	2006-02-09	2006	55806.0
9	住家用	房地(土地+建物)	9.48	住宅大樓(11層含以上有電梯)	2	3	2	有	91.17	有	14	0.00	無	商	左營區	2016-04-07	2016	2000-05-03	2000	43874.0

Last rows

	purpose	trading_target	land_area	property_type	living_room	bedroom	bathroom	partition	property_area	is_managed	total_floor	parking_area	parking_price	parking_type	land_use	district	trading_date	trading_year	built_date	built_year	price_per_sqm
36564	住家用	房地(土地+建物)	4.90	華廈(10層含以下有電梯)	1	1	1	有	36.18	有	8	0.00	0	無	商	左營區	2015-01-02	2015	1995-04-21	1995	38695.0
36565	住家用	房地(土地+建物)	7.49	住宅大樓(11層含以上有電梯)	2	2	1	有	86.27	有	15	0.00	0	無	住	左營區	2016-12-07	2016	2008-01-03	2008	62594.0
36566	住家用	房地(土地+建物)+車位	28.97	住宅大樓(11層含以上有電梯)	2	4	4	有	453.71	有	15	48.82	4600000	坡道平面	商	苓雅區	2017-10-06	2017	2014-11-07	2014	103485.0
36567	住家用	房地(土地+建物)	4.39	華廈(10層含以下有電梯)	1	1	1	有	51.04	有	7	0.00	0	無	住	三民區	2019-02-27	2019	1995-04-28	1995	30035.0
36568	住家用	房地(土地+建物)	8.55	住宅大樓(11層含以上有電梯)	2	3	2	有	84.54	有	15	0.00	0	無	商	楠梓區	2014-12-30	2014	2014-05-09	2014	45541.0
36569	住家用	房地(土地+建物)+車位	19.27	住宅大樓(11層含以上有電梯)	2	4	2	有	220.42	有	15	0.00	0	坡道平面	商	前鎮區	2019-12-15	2019	2008-10-24	2008	52536.0
36570	住家用	房地(土地+建物)+車位	14.60	住宅大樓(11層含以上有電梯)	2	3	2	有	154.81	有	14	0.00	0	坡道平面	商	三民區	2015-11-16	2015	2001-06-15	2001	51676.0
36571	住家用	房地(土地+建物)+車位	20.56	住宅大樓(11層含以上有電梯)	2	3	2	有	168.24	有	15	0.00	0	坡道平面	住	三民區	2015-02-15	2015	1994-01-19	1994	52306.0
36572	住家用	房地(土地+建物)	20.91	住宅大樓(11層含以上有電梯)	1	3	2	有	160.37	有	16	0.00	0	無	住	鼓山區	2015-05-08	2015	1995-01-26	1995	54873.0
36573	住家用	房地(土地+建物)	3.06	套房(1房1廳1衛)	1	1	1	有	47.13	有	21	0.00	0	無	商	左營區	2014-12-18	2014	1995-10-19	1995	33949.0

Most frequently occurring

	purpose	trading_target	land_area	property_type	living_room	bedroom	bathroom	partition	property_area	is_managed	total_floor	parking_area	parking_price	parking_type	land_use	district	trading_date	trading_year	built_date	built_year	price_per_sqm	# duplicates
29	住家用	房地(土地+建物)	33.41	住宅大樓(11層含以上有電梯)	2	3	2	有	173.82	無	14	0.00	0	無	住	左營區	2015-12-11	2015	2010-12-16	2010	20417.0	6
14	住家用	房地(土地+建物)	6.59	華廈(10層含以下有電梯)	1	1	1	有	51.04	有	7	0.00	0	無	住	三民區	2019-02-27	2019	1995-04-28	1995	31368.0	5
7	住家用	房地(土地+建物)	4.39	華廈(10層含以下有電梯)	1	1	1	有	51.04	有	7	0.00	0	無	住	三民區	2019-02-27	2019	1995-04-28	1995	30310.0	3
26	住家用	房地(土地+建物)	9.87	套房(1房1廳1衛)	1	1	1	有	28.27	有	5	0.00	0	無	住	三民區	2020-01-06	2020	2006-03-22	2006	55713.0	3
55	住家用	房地(土地+建物)+車位	32.73	住宅大樓(11層含以上有電梯)	2	3	2	有	263.73	有	15	45.98	3800000	坡道平面	住	左營區	2015-07-27	2015	2014-07-11	2014	74397.0	3
57	商業用	房地(土地+建物)	14.00	住宅大樓(11層含以上有電梯)	0	0	0	無	168.88	有	12	0.00	0	無	商	新興區	2019-08-22	2019	1995-12-14	1995	50924.0	3
0	住商用	房地(土地+建物)	7.17	住宅大樓(11層含以上有電梯)	2	3	3	有	166.76	有	35	0.00	0	無	商	苓雅區	2017-07-06	2017	1997-04-21	1997	44675.0	2
1	住商用	房地(土地+建物)	76.00	透天厝	1	4	5	有	218.53	無	4	0.00	0	無	住	楠梓區	2017-06-06	2017	2007-01-24	2007	73216.0	2
2	住家用	房地(土地+建物)	2.96	住宅大樓(11層含以上有電梯)	1	1	1	有	36.67	有	13	0.00	0	無	商	新興區	2015-06-29	2015	1996-06-03	1996	38860.0	2
3	住家用	房地(土地+建物)	3.19	套房(1房1廳1衛)	0	1	1	有	39.55	有	12	0.00	0	無	商	苓雅區	2016-04-11	2016	1994-10-05	1994	25917.0	2

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Most occurring scripts

Most frequent character per script

Han

Most occurring blocks

Most frequent character per block

CJK

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Open Punctuation

Math Symbol

Close Punctuation

Most occurring scripts

Most frequent character per script

Han

Common

Most occurring blocks

Most frequent character per block

CJK

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Decimal Number

Open Punctuation

Close Punctuation

Most occurring scripts

Most frequent character per script

Han

Common

Most occurring blocks

Most frequent character per block

CJK

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Most occurring scripts

Most frequent character per script

Han

Most occurring blocks

Most frequent character per block

CJK

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Most occurring scripts

Most frequent character per script

Han

Most occurring blocks

Most frequent character per block

CJK

Common Values

Length

Pie chart